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RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Simultaneous clustering of multiple gene expression and physical interaction datasets.

Manikandan Narayanan1, Adrian Vetta, Eric E Schadt

  • 1Department of Genetics, Rosetta Inpharmatics Merck, Seattle, Washington, United States of America. manikandan_narayanan@merck.com

Plos Computational Biology
|April 27, 2010
PubMed
Summary

Integrating multiple genome-wide datasets is challenging. Our JointCluster algorithm simultaneously clusters networks, identifying robust gene sets across diverse biological data for better insights into cellular functions.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Genome-wide datasets offer insights but integrating them is difficult.
  • Coordinated biological functions are often reflected across multiple interaction and activity networks.
  • Existing methods struggle to effectively integrate heterogeneous large-scale biological data.

Purpose of the Study:

  • To develop a robust computational framework for integrating multiple biological networks.
  • To identify gene sets that exhibit coordinated behavior across diverse genomic datasets.
  • To improve the understanding of biological systems by leveraging multi-network integration.

Main Methods:

  • Developed JointCluster, an algorithm for simultaneous clustering of multiple biological networks.
  • Employed techniques for theoretical guarantees on clustering quality and scaling heuristics.
  • Applied the algorithm to integrate gene coexpression and physical interaction networks.

Main Results:

  • JointCluster demonstrated robustness in recovering clusters from networks with high false positive rates.
  • The algorithm outperformed alternative methods in analyzing yeast gene expression and physical interaction data.
  • Discovered robust gene clusters consistently enriched for biological functions and improving gene coverage.

Conclusions:

  • JointCluster provides an efficient and theoretically grounded method for multi-network integration.
  • The identified clusters offer insights into yeast biology, coordinated transcription, and uncharacterized genes.
  • This approach advances the integration of large-scale biological datasets for comprehensive systems analysis.